R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(120.3,0,133.4,0,109.4,0,93.2,0,91.2,0,99.2,0,108.2,0,101.5,0,106.9,0,104.4,0,77.9,0,60,0,99.5,0,95,0,105.6,0,102.5,0,93.3,0,97.3,0,127,0,111.7,0,96.4,0,133,0,72.2,0,95.8,0,124.1,0,127.6,0,110.7,0,104.6,0,112.7,0,115.3,0,139.4,0,119,0,97.4,0,154,0,81.5,0,88.8,0,127.7,1,105.1,1,114.9,1,106.4,1,104.5,1,121.6,1,141.4,1,99,1,126.7,1,134.1,1,81.3,1,88.6,1,132.7,1,132.9,1,134.4,1,103.7,1,119.7,1,115,1,132.9,1,108.5,1,113.9,1,142.9,1,95.2,1,93,1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 120.3 0 1 0 0 0 0 0 0 0 0 0 0
2 133.4 0 0 1 0 0 0 0 0 0 0 0 0
3 109.4 0 0 0 1 0 0 0 0 0 0 0 0
4 93.2 0 0 0 0 1 0 0 0 0 0 0 0
5 91.2 0 0 0 0 0 1 0 0 0 0 0 0
6 99.2 0 0 0 0 0 0 1 0 0 0 0 0
7 108.2 0 0 0 0 0 0 0 1 0 0 0 0
8 101.5 0 0 0 0 0 0 0 0 1 0 0 0
9 106.9 0 0 0 0 0 0 0 0 0 1 0 0
10 104.4 0 0 0 0 0 0 0 0 0 0 1 0
11 77.9 0 0 0 0 0 0 0 0 0 0 0 1
12 60.0 0 0 0 0 0 0 0 0 0 0 0 0
13 99.5 0 1 0 0 0 0 0 0 0 0 0 0
14 95.0 0 0 1 0 0 0 0 0 0 0 0 0
15 105.6 0 0 0 1 0 0 0 0 0 0 0 0
16 102.5 0 0 0 0 1 0 0 0 0 0 0 0
17 93.3 0 0 0 0 0 1 0 0 0 0 0 0
18 97.3 0 0 0 0 0 0 1 0 0 0 0 0
19 127.0 0 0 0 0 0 0 0 1 0 0 0 0
20 111.7 0 0 0 0 0 0 0 0 1 0 0 0
21 96.4 0 0 0 0 0 0 0 0 0 1 0 0
22 133.0 0 0 0 0 0 0 0 0 0 0 1 0
23 72.2 0 0 0 0 0 0 0 0 0 0 0 1
24 95.8 0 0 0 0 0 0 0 0 0 0 0 0
25 124.1 0 1 0 0 0 0 0 0 0 0 0 0
26 127.6 0 0 1 0 0 0 0 0 0 0 0 0
27 110.7 0 0 0 1 0 0 0 0 0 0 0 0
28 104.6 0 0 0 0 1 0 0 0 0 0 0 0
29 112.7 0 0 0 0 0 1 0 0 0 0 0 0
30 115.3 0 0 0 0 0 0 1 0 0 0 0 0
31 139.4 0 0 0 0 0 0 0 1 0 0 0 0
32 119.0 0 0 0 0 0 0 0 0 1 0 0 0
33 97.4 0 0 0 0 0 0 0 0 0 1 0 0
34 154.0 0 0 0 0 0 0 0 0 0 0 1 0
35 81.5 0 0 0 0 0 0 0 0 0 0 0 1
36 88.8 0 0 0 0 0 0 0 0 0 0 0 0
37 127.7 1 1 0 0 0 0 0 0 0 0 0 0
38 105.1 1 0 1 0 0 0 0 0 0 0 0 0
39 114.9 1 0 0 1 0 0 0 0 0 0 0 0
40 106.4 1 0 0 0 1 0 0 0 0 0 0 0
41 104.5 1 0 0 0 0 1 0 0 0 0 0 0
42 121.6 1 0 0 0 0 0 1 0 0 0 0 0
43 141.4 1 0 0 0 0 0 0 1 0 0 0 0
44 99.0 1 0 0 0 0 0 0 0 1 0 0 0
45 126.7 1 0 0 0 0 0 0 0 0 1 0 0
46 134.1 1 0 0 0 0 0 0 0 0 0 1 0
47 81.3 1 0 0 0 0 0 0 0 0 0 0 1
48 88.6 1 0 0 0 0 0 0 0 0 0 0 0
49 132.7 1 1 0 0 0 0 0 0 0 0 0 0
50 132.9 1 0 1 0 0 0 0 0 0 0 0 0
51 134.4 1 0 0 1 0 0 0 0 0 0 0 0
52 103.7 1 0 0 0 1 0 0 0 0 0 0 0
53 119.7 1 0 0 0 0 1 0 0 0 0 0 0
54 115.0 1 0 0 0 0 0 1 0 0 0 0 0
55 132.9 1 0 0 0 0 0 0 1 0 0 0 0
56 108.5 1 0 0 0 0 0 0 0 1 0 0 0
57 113.9 1 0 0 0 0 0 0 0 0 1 0 0
58 142.9 1 0 0 0 0 0 0 0 0 0 1 0
59 95.2 1 0 0 0 0 0 0 0 0 0 0 1
60 93.0 1 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
81.305 9.838 35.620 33.560 29.760 16.840
M5 M6 M7 M8 M9 M10
19.040 24.440 44.540 22.700 23.020 48.440
M11
-3.620
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-25.34500 -5.76250 -0.02375 7.25500 24.25500
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 81.305 5.306 15.324 < 2e-16 ***
X 9.838 3.043 3.233 0.002244 **
M1 35.620 7.304 4.877 1.27e-05 ***
M2 33.560 7.304 4.595 3.26e-05 ***
M3 29.760 7.304 4.075 0.000176 ***
M4 16.840 7.304 2.306 0.025585 *
M5 19.040 7.304 2.607 0.012202 *
M6 24.440 7.304 3.346 0.001618 **
M7 44.540 7.304 6.098 1.91e-07 ***
M8 22.700 7.304 3.108 0.003193 **
M9 23.020 7.304 3.152 0.002823 **
M10 48.440 7.304 6.632 2.95e-08 ***
M11 -3.620 7.304 -0.496 0.622450
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.55 on 47 degrees of freedom
Multiple R-squared: 0.7033, Adjusted R-squared: 0.6276
F-statistic: 9.285 on 12 and 47 DF, p-value: 8.063e-09
> postscript(file="/var/www/html/freestat/rcomp/tmp/1f6cf1229785781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2ie181229785781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3sk821229785781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4o3bw1229785781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5zgl51229785781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6 7 8
3.3750 18.5350 -1.6650 -4.9450 -9.1450 -6.5450 -17.6450 -2.5050
9 10 11 12 13 14 15 16
2.5750 -25.3450 0.2150 -21.3050 -17.4250 -19.8650 -5.4650 4.3550
17 18 19 20 21 22 23 24
-7.0450 -8.4450 1.1550 7.6950 -7.9250 3.2550 -5.4850 14.4950
25 26 27 28 29 30 31 32
7.1750 12.7350 -0.3650 6.4550 12.3550 9.5550 13.5550 14.9950
33 34 35 36 37 38 39 40
-6.9250 24.2550 3.8150 7.4950 0.9375 -19.6025 -6.0025 -1.5825
41 42 43 44 45 46 47 48
-5.6825 6.0175 5.7175 -14.8425 12.5375 -5.4825 -6.2225 -2.5425
49 50 51 52 53 54 55 56
5.9375 8.1975 13.4975 -4.2825 9.5175 -0.5825 -2.7825 -5.3425
57 58 59 60
-0.2625 3.3175 7.6775 1.8575
> postscript(file="/var/www/html/freestat/rcomp/tmp/6ushv1229785781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 3.3750 NA
1 18.5350 3.3750
2 -1.6650 18.5350
3 -4.9450 -1.6650
4 -9.1450 -4.9450
5 -6.5450 -9.1450
6 -17.6450 -6.5450
7 -2.5050 -17.6450
8 2.5750 -2.5050
9 -25.3450 2.5750
10 0.2150 -25.3450
11 -21.3050 0.2150
12 -17.4250 -21.3050
13 -19.8650 -17.4250
14 -5.4650 -19.8650
15 4.3550 -5.4650
16 -7.0450 4.3550
17 -8.4450 -7.0450
18 1.1550 -8.4450
19 7.6950 1.1550
20 -7.9250 7.6950
21 3.2550 -7.9250
22 -5.4850 3.2550
23 14.4950 -5.4850
24 7.1750 14.4950
25 12.7350 7.1750
26 -0.3650 12.7350
27 6.4550 -0.3650
28 12.3550 6.4550
29 9.5550 12.3550
30 13.5550 9.5550
31 14.9950 13.5550
32 -6.9250 14.9950
33 24.2550 -6.9250
34 3.8150 24.2550
35 7.4950 3.8150
36 0.9375 7.4950
37 -19.6025 0.9375
38 -6.0025 -19.6025
39 -1.5825 -6.0025
40 -5.6825 -1.5825
41 6.0175 -5.6825
42 5.7175 6.0175
43 -14.8425 5.7175
44 12.5375 -14.8425
45 -5.4825 12.5375
46 -6.2225 -5.4825
47 -2.5425 -6.2225
48 5.9375 -2.5425
49 8.1975 5.9375
50 13.4975 8.1975
51 -4.2825 13.4975
52 9.5175 -4.2825
53 -0.5825 9.5175
54 -2.7825 -0.5825
55 -5.3425 -2.7825
56 -0.2625 -5.3425
57 3.3175 -0.2625
58 7.6775 3.3175
59 1.8575 7.6775
60 NA 1.8575
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 18.5350 3.3750
[2,] -1.6650 18.5350
[3,] -4.9450 -1.6650
[4,] -9.1450 -4.9450
[5,] -6.5450 -9.1450
[6,] -17.6450 -6.5450
[7,] -2.5050 -17.6450
[8,] 2.5750 -2.5050
[9,] -25.3450 2.5750
[10,] 0.2150 -25.3450
[11,] -21.3050 0.2150
[12,] -17.4250 -21.3050
[13,] -19.8650 -17.4250
[14,] -5.4650 -19.8650
[15,] 4.3550 -5.4650
[16,] -7.0450 4.3550
[17,] -8.4450 -7.0450
[18,] 1.1550 -8.4450
[19,] 7.6950 1.1550
[20,] -7.9250 7.6950
[21,] 3.2550 -7.9250
[22,] -5.4850 3.2550
[23,] 14.4950 -5.4850
[24,] 7.1750 14.4950
[25,] 12.7350 7.1750
[26,] -0.3650 12.7350
[27,] 6.4550 -0.3650
[28,] 12.3550 6.4550
[29,] 9.5550 12.3550
[30,] 13.5550 9.5550
[31,] 14.9950 13.5550
[32,] -6.9250 14.9950
[33,] 24.2550 -6.9250
[34,] 3.8150 24.2550
[35,] 7.4950 3.8150
[36,] 0.9375 7.4950
[37,] -19.6025 0.9375
[38,] -6.0025 -19.6025
[39,] -1.5825 -6.0025
[40,] -5.6825 -1.5825
[41,] 6.0175 -5.6825
[42,] 5.7175 6.0175
[43,] -14.8425 5.7175
[44,] 12.5375 -14.8425
[45,] -5.4825 12.5375
[46,] -6.2225 -5.4825
[47,] -2.5425 -6.2225
[48,] 5.9375 -2.5425
[49,] 8.1975 5.9375
[50,] 13.4975 8.1975
[51,] -4.2825 13.4975
[52,] 9.5175 -4.2825
[53,] -0.5825 9.5175
[54,] -2.7825 -0.5825
[55,] -5.3425 -2.7825
[56,] -0.2625 -5.3425
[57,] 3.3175 -0.2625
[58,] 7.6775 3.3175
[59,] 1.8575 7.6775
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 18.5350 3.3750
2 -1.6650 18.5350
3 -4.9450 -1.6650
4 -9.1450 -4.9450
5 -6.5450 -9.1450
6 -17.6450 -6.5450
7 -2.5050 -17.6450
8 2.5750 -2.5050
9 -25.3450 2.5750
10 0.2150 -25.3450
11 -21.3050 0.2150
12 -17.4250 -21.3050
13 -19.8650 -17.4250
14 -5.4650 -19.8650
15 4.3550 -5.4650
16 -7.0450 4.3550
17 -8.4450 -7.0450
18 1.1550 -8.4450
19 7.6950 1.1550
20 -7.9250 7.6950
21 3.2550 -7.9250
22 -5.4850 3.2550
23 14.4950 -5.4850
24 7.1750 14.4950
25 12.7350 7.1750
26 -0.3650 12.7350
27 6.4550 -0.3650
28 12.3550 6.4550
29 9.5550 12.3550
30 13.5550 9.5550
31 14.9950 13.5550
32 -6.9250 14.9950
33 24.2550 -6.9250
34 3.8150 24.2550
35 7.4950 3.8150
36 0.9375 7.4950
37 -19.6025 0.9375
38 -6.0025 -19.6025
39 -1.5825 -6.0025
40 -5.6825 -1.5825
41 6.0175 -5.6825
42 5.7175 6.0175
43 -14.8425 5.7175
44 12.5375 -14.8425
45 -5.4825 12.5375
46 -6.2225 -5.4825
47 -2.5425 -6.2225
48 5.9375 -2.5425
49 8.1975 5.9375
50 13.4975 8.1975
51 -4.2825 13.4975
52 9.5175 -4.2825
53 -0.5825 9.5175
54 -2.7825 -0.5825
55 -5.3425 -2.7825
56 -0.2625 -5.3425
57 3.3175 -0.2625
58 7.6775 3.3175
59 1.8575 7.6775
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/7hral1229785781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/84vsr1229785781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9tq361229785781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/10c3x91229785781.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/11wh7m1229785781.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/12t7of1229785781.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/139gxl1229785782.tab")
>
> system("convert tmp/1f6cf1229785781.ps tmp/1f6cf1229785781.png")
> system("convert tmp/2ie181229785781.ps tmp/2ie181229785781.png")
> system("convert tmp/3sk821229785781.ps tmp/3sk821229785781.png")
> system("convert tmp/4o3bw1229785781.ps tmp/4o3bw1229785781.png")
> system("convert tmp/5zgl51229785781.ps tmp/5zgl51229785781.png")
> system("convert tmp/6ushv1229785781.ps tmp/6ushv1229785781.png")
> system("convert tmp/7hral1229785781.ps tmp/7hral1229785781.png")
> system("convert tmp/84vsr1229785781.ps tmp/84vsr1229785781.png")
> system("convert tmp/9tq361229785781.ps tmp/9tq361229785781.png")
>
>
> proc.time()
user system elapsed
3.072 2.307 4.340